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A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features

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Figure

Figure 1: The holistic structure of TATGM. The dotted arrow denotes that the texts which are generated by thesequence modeling decoder are fed into the topic modeling encoder as the input of the discriminator.
Table 1: Summary statistics for datasets. SM Voc and TM Voc stand for vocabularies of corpus in the sequencemodeling component and the topic modeling component, respectively.
Table 2: Language modeling results in the terms of PPL and KL.
Table 5: A topic transfer example of one question-answering pair on Yahoo dataset.

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